AI Can Do A Lot Of Things, But It Sucks At Picking Fights On Social Media

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AI has already dominated humans in tasks like chess and mathematics, and is increasingly gaining ground in fields like programming, advertising, and even therapy. But researchers think there is one frontier AI hasn’t been able to cross yet: being authentically toxic on the internet.

A recent paper from the University of Zurich, University of Amsterdam, Duke University, and New York University finds that social media posts generated by a variety of LLMs are “readily distinguishable” from those made by humans with 70–80% accuracy, “well above chance.”

Researchers tested nine open-weight LLMs from six distinct model families (Apertus, DeepSeek, Gemma, Llama, Mistral, and Qwen), plus a large-scale Llama model, across Bluesky, Reddit, and X. The study, first reported by Ars Technica, found that the “toxicity score” of posts on these platforms consistently emerged as a key discriminator between AI and human posts.

Translation: If someone responds to your post with a particularly hilarious or cutting zinger, it was probably written by a human.

“These results suggest that while LLMs can reproduce the form of online dialogue, they struggle to capture its feeling: the spontaneous, affect-laden expression characteristic of human interaction,” said the researchers.

The study suggests LLMs are better at imitating the technical aspects of social media posts, like sentence length or word count, rather than expressing emotions. Across all three platforms, average toxicity scores were lower in the AI responses than in the authentic human replies.

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The findings come after AI users complained earlier this year that ChatGPT’s 4o model had become too sycophantic. GPT-5, however, overcorrected on that front, prompting OpenAI to re-release the more friendly GPT-4o after people lost their minds over GPT-5’s curt responses.

Researchers also found that AI models whose responses had not been fine-tuned by human instruction—such as Llama-3.1-8B, Mistral-7B, and Apertus-8B—outperformed their instruction-tuned counterparts. They suggest this may indicate that alignment training “introduces stylistic regularities that make text more, rather than less, machine-like.”

The models struggled most when generating posts in certain contexts. For example, expressing positive emotions on Elon Musk’s X or Bluesky, or discussing politics on Reddit. On the whole, all the AI models tested performed better when imitating posts on X compared to Bluesky, with Reddit posts being the hardest of the three to imitate, since “conversational norms are more diverse” on the site, according to researchers.